Andrew Bell, Ron Johnston and Kelvyn Jones andrew.bell@bristol.ac.uk

1. School of Geographical Sciences Stylised fact or situated messiness? A multilevel country panel analysis of the effects of debt on national economic growth, using Reinhart and Rogoff’s data Andrew Bell, Ron Johnston and Kelvyn Jones andrew.bell@bristol.ac.uk NCRM Research Methods Festival, July 2014

2. Outline • Reinhart and Rogoff - Growth in a time of debt • Herndon et al’s critique • What is missing? • Our analysis • Random coefficients model • Multilevel distributed lag model

3. Key methodological point • The world is complex, and needs realistically complex models to represent it • Aiming for an average effect, or ‘stylised fact’ can be very misleading when relationships are heterogeneous over time or space

4. Growth in a Time of Debt (2010) • Amer Econ Rev 100(2) 573-78 • Reinhart and Rogoff argue for a threshold debt value at 90% of GDP, after which growth dramatically declines in rich countries. • Entirely descriptive – no statistical model

5. Influence • A key citation and influence for those in favour of austerity budgets • “As Rogoff and Reinhart demonstrate convincingly, all financial crises ultimately have their origins in one thing.” (George Osborne, 2010) • “conclusive empirical evidence that gross debt …exceeding 90 percent of the economy has a significant negative effect on economic growth.” (Paul Ryan, 2013, p78)

6. Herndon et al’s critique • Camb J Econ, 2014, 38(2), 257-279 • Find three key flaws • An excel spreadsheet error deleting five countries at the top of the alphabet • Weighting by country, not by country-year • Exclusion of certain data points • It seems that the combination of the second two are what produced the apparent threshold effect

7. Herndon et al’s critique • When corrected, change is much less extreme – no threshold – growth declines gradually with debt • But still an apparent relationship – growth declines with debt.

8. Herndon et al’s critique

9. What is missing? • ‘Stylised fact’ of a single un-varying effect is too simplistic • Why should the effect of debt be the same in Japan as in the USA? • Assumption that debt leads to growth and not vice-versa

10. Increase in Deficit More govt borrowing Interest rates up Growth reduced Investors wary of govt ability to make repayments Investor flight Reduced government revenue Increase in Debt Government spends to stimulate growth Direction of causality

11. Our reanalysis – 2 parts • Multilevel model that allows the growth-debt relationship to vary between countries (random slopes model) • Multilevel ‘distributed lag model’ that gives evidence of direction of causality • does growth go up after debt, or before?

12. Random slopes model • Run in MLwiN • Model additionally run using RR’s 4 groupings (instead of a linear effect) – results substantively similar

13. Random slopes model Average effect of debt (within and between effects separated – see Bell and Jones 2014) • Run in MLwiN • Model additionally run using RR’s 4 groupings (instead of a linear effect) – results substantively similar

14. Random slopes model Average effect of debt (within and between effects separated – see Bell and Jones 2014) Varying effects of debt across countries • Run in MLwiN • Model additionally run using RR’s 4 groupings (instead of a linear effect) – results substantively similar

15. Random slopes model Average effect of debt (within and between effects separated – see Bell and Jones 2014) Varying effects of debt across countries Occasion-level variance (that is, volatility) varies with debt • Run in MLwiN • Model additionally run using RR’s 4 groupings (instead of a linear effect) – results substantively similar

16. Random slopes model Average effect of debt (within and between effects separated – see Bell and Jones 2014) Varying effects of debt across countries Occasion-level variance (that is, volatility) varies with debt Year controlled in all parts of model • Run in MLwiN • Model additionally run using RR’s 4 groupings (instead of a linear effect) – results substantively similar

17. Distributed lag model • Regress multiple leads and lags of debt on growth • Can plot these in an ‘impulse response’ graph • See whether a change in growth or a change in debt happens first From http://www.nextnewdeal.net/rortybomb/guest-post-reinhartrogoff-and-growth-time-debt

18. Distributed lag model • Dube (2013) – reanalyses RR’s data, finds evidence direction is mainly in the direction from growth to debt, not from debt to growth • But is this the same for all countries? • Use the multilevel logic of previous model to allow causal direction to vary by country… From http://www.nextnewdeal.net/rortybomb/guest-post-reinhartrogoff-and-growth-time-debt

19. Multilevel distributed lag model . Run in Stata using the runmlwin command (code available on request)

20. 6 5 4 Predicted Growth (%GDP) Greece 3 Australia UK Ireland US Japan 2 1 0 0 70 140 210 Debt:GDP ratio Results (1) • Average effect (the “stylised fact”) now not significant • Lots of variation between countries • No evidence of a relationship between growth and debt in the UK

21. Results (2) • Higher level-1 variance at debt ratios greater than 90% • Suggests debt is associated with volatility in economic growth

22. Results (3) In most countries, a change in debt occurs after a change in growth Suggests low growth causes debt, rather than debt causing growth. Some variation – e.g. less clear directionality in Ireland.

23. Conclusions • Substantive: • The relationship between growth and debt is highly variable; • The average effect (‘stylised fact’) is not significant, although volatility in growth does appear to be higher at debt:GDP ratios over 90%; • The causal direction is predominantly from growth to debt, not debt to growth • Methodological: • Stylised facts are often too simplistic – the world is complex and messy, and our statistical models should aim to reflect that complexity.

24. For more information • Bell, A; Johnston, R; Jones K (2014) Stylised fact or situated messiness? The diverse effects of increasing debt on national economic growth. Journal of Economic Geography,online, DOI: 10.1093/jeg/lbu005 • Bell, A and Jones, K (2014) Explaining fixed effects: Random effects modelling of time-series cross-sectional and panel data. Political Science Research and Methods, online, DOI: 10.1017/psrm.2014.7 • Paper showing the advantages of using a multilevel/random effects model, rather than fixed effects models or other alternatives